The quest to understand learning and intelligence, whether in humans or machines, has always been a motivating force in the field from its early beginnings. Indeed, the psychologist Charles Spearman, influenced by Francis Galton's development of the correlation coefficient, introduced factor analysis in 1904 as a statistical method to study the organization of mental abilities in humans [1]}. He proposed that from the correlations of multiple observed variables (e.g. tests of general ability), we can infer a single latent factor that can explain the common variance shared among all the observations. He referred to the latent variable common to all the measured ability tests as g, the general factor of intelligence.
